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Data centres increase their size and complexity due to the increasing amount of heterogeneous workloads and patterns to be served. Such a mix of various purpose workloads makes the optimisation of resource management systems according to temporal or application-level patterns difficult. Data-centre operators have developed multiple resource-management models to improve scheduling performance in controlled scenarios. However, the constant evolution of the workloads makes the utilisation of only one resource-management model sub-optimal in some scenarios.In this work, we propose: (a) a machine learning regression model based on gradient boosting to predict the time a resource manager needs to schedule incoming jobs for a given period; and (b) a resource management model, Boost, that takes advantage of this regression model to predict the scheduling time of a catalogue of resource managers so that the most performant can be used for a time span.The benefits of the proposed resource-management model are analysed by comparing its scheduling performance KPIs to those provided by the two most popular resource-management models: two-level, used by Apache Mesos, and shared-state, employed by Google Borg. Such gains are empirically evaluated by simulating a hyper-scale data centre that executes a realistic synthetically generated workload that follows real-world trace patterns.

Abstract Social polarisation processes have become a central phenomenon for the explanation of population behavioural dynamics in today's societies. Although recent works offer solutions for the detection of polarised political communities in social media, there is still a lack of works that allow an adequate characterization of the specific topics on which these divides between social groups are articulated. Our study aims to discover and characterise antagonistic communities on Twitter based on a method that combines the identification of authorities and textual classifiers around three public debates that have recently produced major controversies: (1) vaccination; (2) climate change; and (3) abortion. The proposed method allows the capture of polarised communities with little effort, requiring only the selection of some terms that characterise the topic and some initial authorities. Our findings show that the processes of social polarisation can vary considerably depending on the subject on which the debates are articulated. Specifically, polarisation manifests more prominently in the realms of vaccination and abortion, whereas this divide is less apparent in the context of climate change.

The detection of communities in social networks is a task with multiple applications both in research and in sectors such as marketing and politics among others. In this paper, we address the task of detecting on-line communities of Twitter users for a given domain. Our main contribution consists in modelling the community detection problem as a biclustering task. We have performed the experimentation with data from the political domain, a very dynamic area with a large number of interested users and a high availability of tweets. We have evaluated our proposal using both extrinsic and intrinsic methods, reaching very good results in both cases. We use the silhouette coefficient as intrinsic metric for clustering evaluation, and a classification task of political leanings of Twitter users as extrinsic evaluation. One of the most interesting conclusions of our experiments is the quality, from the point of view of predictive capacity in the classification task, of the communities identified with the proposed method. The information provided by communities detected through “follow” relationships has a predictive capacity comparable to that of the contents of tweets written by users. The results also show how detected communities can give insights about future events related to these communities that arise around social networks.

In the fight against Malaria, new strategies need to be developed to avoid resistance of the parasite to pharmaceutics and other prevention barriers. Recently, a Host Directed Therapy approach based on the suppression of the starting materials uptake from the host by the parasite has provided excellent results. In this article, we propose the synthesis of bioisosteric compounds that are capable of inhibiting Plasmodium falciparum Choline Kinase and therefore to reduce choline uptake, which is essential for the development of the parasite. Of the 41 bioisosteric compounds reported herein, none showed any influence of the linker on the antimalarial and enzyme inhibitory activity, whereas an effect of the type of cationic heads used could be observed. SARs determined that the thienopyrimidine substituted in 4 by a pyrrolidine is the best scaffold, independently of the chosen linker. The decrease in lipophilicity seems to improve the antimalarial activity but to cause an opposite effect on the inhibition of the enzyme. While potent compounds with similar good inhibitory values have been related to the proposed mechanism of action, some of them still show discrepancies and further studies are needed to determine their specific molecular target.